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Health Checkup results show 'normal ranges,' but these are population-wide standards. If a man in his 50s sees his fasting blood sugar climb by 5 points each year to reach 99 (within 'normal range'), that's actually a warning sign—but current checkup systems don't flag it. Comparing past results requires manually pulling out PDFs and transferring data to a spreadsheet.
Users input past Health Checkup results by year (PDF upload with OCR auto-extraction), and the tool visualizes personal trends for each metric (blood sugar, blood pressure, cholesterol, liver enzymes, etc.) as graphs. It automatically detects metrics that have sharply deviated from the user's personal baseline, providing personalized alerts like 'This metric has been rising for 3 consecutive years → internal medicine consultation recommended.'
| N Novelty | 1-5 | How uncommon the service is in market context. |
| U Urgency | 1-5 | How urgently users need this problem solved now. |
| M Market | 1-5 | Market size and growth potential from proxy indicators. |
| R Realizability | 1-5 | Buildability for a small team with realistic constraints. |
| V Validation | 1-5 | Validation signal quality from competition and demand data. |
| Tech Complexity | / 40 | Difficulty of core implementation stack. |
| Data Availability | / 25 | Practical availability and cost of required data. |
| MVP Timeline | / 20 | Expected time to ship a usable MVP. |
| API Bonus | / 15 | Bonus for viable public API leverage. |
| Competition | / 20 | Signal quality from competitor landscape. |
| Market Demand | / 20 | Demand proxies from search and mention patterns. |
| Timing | / 20 | Fit with current shifts in tech, behavior, and regulation. |
| Revenue Signals | / 15 | Reference evidence for monetization viability. |
| Pick-Axe Fit | / 15 | How well the concept serves participants in a trend. |
| Solo Buildability | / 10 | Practicality for lean-team implementation. |